• Huai-Che Yang, Chih-Chun Wu, Cheng-Chia Lee, Huai-En Huang, Wei-Kai Lee, Wen-Yuh Chung, Hsiu-Mei Wu, Wan-Yuo Guo, Yu-Te Wu, and Chia-Feng Lu.
• Department of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital, Taiwan; School of Medicine, National Yang-Ming University, Taipei, Taiwan.
• Radiother Oncol. 2021 Feb 1; 155: 123-130.

Background And PurposeGamma Knife radiosurgery (GKRS) is a safe and effective treatment modality with a long-term tumor control rate over 90% for vestibular schwannoma (VS). However, numerous tumors may undergo a transient pseudoprogression during 6-18 months after GKRS followed by a long-term volume reduction. The aim of this study is to determine whether the radiomics analysis based on preradiosurgical MRI data could predict the pseudoprogression and long-term outcome of VS after GKRS.Materials And MethodsA longitudinal dataset of patients with VS treated by single GKRS were retrospectively collected. Overall 336 patients with no previous craniotomy for tumor removal and a median of 65-month follow-up period after radiosurgery were finally included in this study. In total 1763 radiomic features were extracted from the multiparameteric MRI data before GKRS followed by the machine-learning classification.ResultsWe constructed a two-level machine-learning model to predict the long-term outcome and the occurrence of transient pseudoprogression after GKRS separately. The prediction of long-term outcome achieved an accuracy of 88.4% based on five radiomic features describing the variation of T2-weighted intensity and inhomogeneity of contrast enhancement in tumor. The prediction of transient pseudoprogression achieved an accuracy of 85.0% based on another five radiomic features associated with the inhomogeneous hypointensity pattern of contrast enhancement and the variation of T2-weighted intensity.ConclusionThe proposed machine-learning model based on the preradiosurgical MR radiomics provides a potential to predict the pseudoprogression and long-term outcome of VS after GKRS, which can benefit the treatment strategy in clinical practice.Copyright © 2020 Elsevier B.V. All rights reserved.

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